Furthermore, as online continual learning has a non-stationary data stream, the model is likely to face the imbalanced training set (where the training samples are mostly from the newly added classes), thus the softmax classifier would suffer from the class-imbalanced problem [1]}. We hence adopt a specifically-designed sampling strategy to balance the learning between old and new classes. With conducting experiments on several datasets and different settings of online continual learning, our full model equipped with all the aforementioned designs (named as SDAF) is demonstrated to provide the state-of-the-art performance in comparison to various baselines.
